

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
A_			:					List[Any]    			=     logging.get_logger(__name__)

def 		UpperCamelCase   (lowercase_:					Union[str, Any]   )	-> List[Any]:
	A__				:       Dict	  =						SwinConfig(
	    embed_dim=192       , depths=(2, 2, 18, 2)       , num_heads=(6, 12, 24, 48)       , window_size=12       , out_features=["""stage2""", """stage3""", """stage4"""]       , )

	A__				:       Any	  =						DetaConfig(
	    backbone_config=a_       , num_queries=900       , encoder_ffn_dim=2048       , decoder_ffn_dim=2048       , num_feature_levels=5       , assign_first_stage=a_       , with_box_refine=a_       , two_stage=a_       , )

	# set labels
	A__				:       Optional[int]	  =						"huggingface/label-files"
	if "o365" in model_name:
		A__				:       List[str]	  =						366
		A__				:       Any	  =						"object365-id2label.json"
	else:
		A__				:       Optional[Any]	  =						91
		A__				:       Dict	  =						"coco-detection-id2label.json"

	A__				:       Tuple	  =						num_labels
	A__				:       List[Any]	  =						json.load(open(cached_download(hf_hub_url(a_       , a_       , repo_type="""dataset"""   )   )       , """r"""   )   )
	A__				:       List[str]	  =						{int(a_   ): v for k, v in idalabel.items()}
	A__				:       List[str]	  =						idalabel
	A__				:       Union[str, Any]	  =						{v: k for k, v in idalabel.items()}

	return config

def 		UpperCamelCase   (lowercase_:					Optional[Any]   )	-> Union[str, Any]:
	A__				:       Union[str, Any]	  =						[]

	# stem
	# fmt: off
	rename_keys.append(("""backbone.0.body.patch_embed.proj.weight""", """model.backbone.model.embeddings.patch_embeddings.projection.weight""")   )
	rename_keys.append(("""backbone.0.body.patch_embed.proj.bias""", """model.backbone.model.embeddings.patch_embeddings.projection.bias""")   )
	rename_keys.append(("""backbone.0.body.patch_embed.norm.weight""", """model.backbone.model.embeddings.norm.weight""")   )
	rename_keys.append(("""backbone.0.body.patch_embed.norm.bias""", """model.backbone.model.embeddings.norm.bias""")   )
	# stages
	for i in range(len(config.backbone_config.depths   )   ):
		for j in range(config.backbone_config.depths[i]   ):
			rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.norm1.weight""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""")   )
			rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.norm1.bias""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""")   )
			rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""")   )
			rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""")   )
			rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""")   )
			rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""")   )
			rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.norm2.weight""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""")   )
			rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.norm2.bias""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""")   )
			rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""")   )
			rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""")   )
			rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight""")   )
			rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias""")   )

		if i < 3:
			rename_keys.append((f"""backbone.0.body.layers.{i}.downsample.reduction.weight""", f"""model.backbone.model.encoder.layers.{i}.downsample.reduction.weight""")   )
			rename_keys.append((f"""backbone.0.body.layers.{i}.downsample.norm.weight""", f"""model.backbone.model.encoder.layers.{i}.downsample.norm.weight""")   )
			rename_keys.append((f"""backbone.0.body.layers.{i}.downsample.norm.bias""", f"""model.backbone.model.encoder.layers.{i}.downsample.norm.bias""")   )

	rename_keys.append(("""backbone.0.body.norm1.weight""", """model.backbone.model.hidden_states_norms.stage2.weight""")   )
	rename_keys.append(("""backbone.0.body.norm1.bias""", """model.backbone.model.hidden_states_norms.stage2.bias""")   )
	rename_keys.append(("""backbone.0.body.norm2.weight""", """model.backbone.model.hidden_states_norms.stage3.weight""")   )
	rename_keys.append(("""backbone.0.body.norm2.bias""", """model.backbone.model.hidden_states_norms.stage3.bias""")   )
	rename_keys.append(("""backbone.0.body.norm3.weight""", """model.backbone.model.hidden_states_norms.stage4.weight""")   )
	rename_keys.append(("""backbone.0.body.norm3.bias""", """model.backbone.model.hidden_states_norms.stage4.bias""")   )

	# transformer encoder
	for i in range(config.encoder_layers   ):
		rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight""", f"""model.encoder.layers.{i}.self_attn.sampling_offsets.weight""")   )
		rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias""", f"""model.encoder.layers.{i}.self_attn.sampling_offsets.bias""")   )
		rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.attention_weights.weight""", f"""model.encoder.layers.{i}.self_attn.attention_weights.weight""")   )
		rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.attention_weights.bias""", f"""model.encoder.layers.{i}.self_attn.attention_weights.bias""")   )
		rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.value_proj.weight""", f"""model.encoder.layers.{i}.self_attn.value_proj.weight""")   )
		rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.value_proj.bias""", f"""model.encoder.layers.{i}.self_attn.value_proj.bias""")   )
		rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.output_proj.weight""", f"""model.encoder.layers.{i}.self_attn.output_proj.weight""")   )
		rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.output_proj.bias""", f"""model.encoder.layers.{i}.self_attn.output_proj.bias""")   )
		rename_keys.append((f"""transformer.encoder.layers.{i}.norm1.weight""", f"""model.encoder.layers.{i}.self_attn_layer_norm.weight""")   )
		rename_keys.append((f"""transformer.encoder.layers.{i}.norm1.bias""", f"""model.encoder.layers.{i}.self_attn_layer_norm.bias""")   )
		rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.weight""", f"""model.encoder.layers.{i}.fc1.weight""")   )
		rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.bias""", f"""model.encoder.layers.{i}.fc1.bias""")   )
		rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.weight""", f"""model.encoder.layers.{i}.fc2.weight""")   )
		rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.bias""", f"""model.encoder.layers.{i}.fc2.bias""")   )
		rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.weight""", f"""model.encoder.layers.{i}.final_layer_norm.weight""")   )
		rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.bias""", f"""model.encoder.layers.{i}.final_layer_norm.bias""")   )

	# transformer decoder
	for i in range(config.decoder_layers   ):
		rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight""", f"""model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight""")   )
		rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias""", f"""model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias""")   )
		rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.attention_weights.weight""", f"""model.decoder.layers.{i}.encoder_attn.attention_weights.weight""")   )
		rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.attention_weights.bias""", f"""model.decoder.layers.{i}.encoder_attn.attention_weights.bias""")   )
		rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.value_proj.weight""", f"""model.decoder.layers.{i}.encoder_attn.value_proj.weight""")   )
		rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.value_proj.bias""", f"""model.decoder.layers.{i}.encoder_attn.value_proj.bias""")   )
		rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.output_proj.weight""", f"""model.decoder.layers.{i}.encoder_attn.output_proj.weight""")   )
		rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.output_proj.bias""", f"""model.decoder.layers.{i}.encoder_attn.output_proj.bias""")   )
		rename_keys.append((f"""transformer.decoder.layers.{i}.norm1.weight""", f"""model.decoder.layers.{i}.encoder_attn_layer_norm.weight""")   )
		rename_keys.append((f"""transformer.decoder.layers.{i}.norm1.bias""", f"""model.decoder.layers.{i}.encoder_attn_layer_norm.bias""")   )
		rename_keys.append((f"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", f"""model.decoder.layers.{i}.self_attn.out_proj.weight""")   )
		rename_keys.append((f"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", f"""model.decoder.layers.{i}.self_attn.out_proj.bias""")   )
		rename_keys.append((f"""transformer.decoder.layers.{i}.norm2.weight""", f"""model.decoder.layers.{i}.self_attn_layer_norm.weight""")   )
		rename_keys.append((f"""transformer.decoder.layers.{i}.norm2.bias""", f"""model.decoder.layers.{i}.self_attn_layer_norm.bias""")   )
		rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.weight""", f"""model.decoder.layers.{i}.fc1.weight""")   )
		rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.bias""", f"""model.decoder.layers.{i}.fc1.bias""")   )
		rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.weight""", f"""model.decoder.layers.{i}.fc2.weight""")   )
		rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.bias""", f"""model.decoder.layers.{i}.fc2.bias""")   )
		rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.weight""", f"""model.decoder.layers.{i}.final_layer_norm.weight""")   )
		rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.bias""", f"""model.decoder.layers.{i}.final_layer_norm.bias""")   )

	# fmt: on

	return rename_keys

def 		UpperCamelCase   (lowercase_:					List[Any]       , lowercase_:					int       , lowercase_:					List[str]   )	-> List[Any]:
	A__				:       Optional[int]	  =						dct.pop(a_   )
	A__				:       Union[str, Any]	  =						val

def 		UpperCamelCase   (lowercase_:					Tuple       , lowercase_:					List[str]   )	-> int:
	A__				:       str	  =						[int(backbone_config.embed_dim * 2**i   ) for i in range(len(backbone_config.depths   )   )]
	for i in range(len(backbone_config.depths   )   ):
		A__				:       Tuple	  =						num_features[i]
		for j in range(backbone_config.depths[i]   ):
			# fmt: off
			# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
			A__				:       str	  =						state_dict.pop(f"""backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight"""   )
			A__				:       Optional[Any]	  =						state_dict.pop(f"""backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias"""   )
			# next, add query, keys and values (in that order) to the state dict
			A__				:       str	  =						in_proj_weight[:dim, :]
			A__				:       str	  =						in_proj_bias[: dim]
			A__				:       int	  =						in_proj_weight[
			    dim : dim * 2, :
			]
			A__				:       Tuple	  =						in_proj_bias[
			    dim : dim * 2
			]
			A__				:       int	  =						in_proj_weight[
			    -dim :, :
			]
			A__				:       int	  =						in_proj_bias[-dim :]
			# fmt: on

def 		UpperCamelCase   (lowercase_:					Optional[int]       , lowercase_:					List[Any]   )	-> Any:
	# transformer decoder self-attention layers
	A__				:       str	  =						config.d_model
	for i in range(config.decoder_layers   ):
		# read in weights + bias of input projection layer of self-attention
		A__				:       Tuple	  =						state_dict.pop(f"""transformer.decoder.layers.{i}.self_attn.in_proj_weight"""   )
		A__				:       int	  =						state_dict.pop(f"""transformer.decoder.layers.{i}.self_attn.in_proj_bias"""   )
		# next, add query, keys and values (in that order) to the state dict
		A__				:       List[Any]	  =						in_proj_weight[:hidden_size, :]
		A__				:       str	  =						in_proj_bias[:hidden_size]
		A__				:       Optional[int]	  =						in_proj_weight[
		    hidden_size : hidden_size * 2, :
		]
		A__				:       int	  =						in_proj_bias[hidden_size : hidden_size * 2]
		A__				:       Dict	  =						in_proj_weight[-hidden_size:, :]
		A__				:       Dict	  =						in_proj_bias[-hidden_size:]

def 		UpperCamelCase   ()	-> Optional[Any]:
	A__				:       List[str]	  =						"http://images.cocodataset.org/val2017/000000039769.jpg"
	A__				:       str	  =						Image.open(requests.get(a_       , stream=a_   ).raw   )

	return im

@torch.no_grad()
def 		UpperCamelCase   (lowercase_:					Dict       , lowercase_:					Tuple       , lowercase_:					Optional[Any]   )	-> Union[str, Any]:
	A__				:       str	  =						get_deta_config(a_   )

	# load original state dict
	if model_name == "deta-swin-large":
		A__				:       Optional[int]	  =						hf_hub_download(repo_id="""nielsr/deta-checkpoints"""       , filename="""adet_swin_ft.pth"""   )
	elif model_name == "deta-swin-large-o365":
		A__				:       Union[str, Any]	  =						hf_hub_download(repo_id="""jozhang97/deta-swin-l-o365"""       , filename="""deta_swin_pt_o365.pth"""   )
	else:
		raise ValueError(f"""Model name {model_name} not supported"""   )

	A__				:       str	  =						torch.load(a_       , map_location="""cpu"""   )["model"]

	# original state dict
	for name, param in state_dict.items():
		print(a_       , param.shape   )

	# rename keys
	A__				:       List[str]	  =						create_rename_keys(a_   )
	for src, dest in rename_keys:
		rename_key(a_       , a_       , a_   )
	read_in_swin_q_k_v(a_       , config.backbone_config   )
	read_in_decoder_q_k_v(a_       , a_   )

	# fix some prefixes
	for key in state_dict.copy().keys():
		if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
			A__				:       int	  =						state_dict.pop(a_   )
			A__				:       Tuple	  =						val
		if "input_proj" in key:
			A__				:       str	  =						state_dict.pop(a_   )
			A__				:       Dict	  =						val
		if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
			A__				:       List[Any]	  =						state_dict.pop(a_   )
			A__				:       Dict	  =						val

    # finally, create HuggingFace model and load state dict
	A__				:       str	  =						DetaForObjectDetection(a_   )
	model.load_state_dict(a_   )
	model.eval()

	A__				:       List[Any]	  =						"cuda" if torch.cuda.is_available() else "cpu"
	model.to(a_   )

	# load image processor
	A__				:       List[str]	  =						DetaImageProcessor(format="""coco_detection"""   )

	# verify our conversion on image
	A__				:       Tuple	  =						prepare_img()
	A__				:       Optional[int]	  =						processor(images=a_       , return_tensors="""pt"""   )
	A__				:       Any	  =						encoding["pixel_values"]
	A__				:       int	  =						model(pixel_values.to(a_   )   )

	# verify logits
	print("""Logits:"""       , outputs.logits[0, :3, :3]   )
	print("""Boxes:"""       , outputs.pred_boxes[0, :3, :3]   )
	if model_name == "deta-swin-large":
		A__				:       int	  =						torch.tensor(
		    [[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]]   )
		A__				:       Optional[int]	  =						torch.tensor([[0.4987, 0.4969, 0.9999], [0.2549, 0.5498, 0.4805], [0.5498, 0.2757, 0.0569]]   )
	elif model_name == "deta-swin-large-o365":
		A__				:       Optional[Any]	  =						torch.tensor(
		    [[-8.0122, -3.5720, -4.9717], [-8.1547, -3.6886, -4.6389], [-7.6610, -3.6194, -5.0134]]   )
		A__				:       List[Any]	  =						torch.tensor([[0.2523, 0.5549, 0.4881], [0.7715, 0.4149, 0.4601], [0.5503, 0.2753, 0.0575]]   )
	assert torch.allclose(outputs.logits[0, :3, :3]       , expected_logits.to(a_   )       , atol=1E-4   )
	assert torch.allclose(outputs.pred_boxes[0, :3, :3]       , expected_boxes.to(a_   )       , atol=1E-4   )
	print("""Everything ok!"""   )

	if pytorch_dump_folder_path:
		# Save model and processor
		logger.info(f"""Saving PyTorch model and processor to {pytorch_dump_folder_path}..."""   )
		Path(a_   ).mkdir(exist_ok=a_   )
		model.save_pretrained(a_   )
		processor.save_pretrained(a_   )

	# Push to hub
	if push_to_hub:
		print("""Pushing model and processor to hub..."""   )
		model.push_to_hub(f"""jozhang97/{model_name}"""   )
		processor.push_to_hub(f"""jozhang97/{model_name}"""   )


if __name__ == "__main__":
	A_			:					Tuple    			=     argparse.ArgumentParser()

	parser.add_argument(
	    '--model_name',
	    type=str,
	    default='deta-swin-large',
	    choices=['deta-swin-large', 'deta-swin-large-o365'],
	    help='Name of the model you\'d like to convert.',
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path',
	    default=None,
	    type=str,
	    help='Path to the folder to output PyTorch model.',
	)
	parser.add_argument(
	    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
	)
	A_			:					List[Any]    			=     parser.parse_args()
	convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)










'''simple docstring'''






lowerCAmelCase									=   [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(10_00_00)]






def   __A    (				a_    :	int						):
		lowerCAmelCase  :     Optional[int]       						= 0
		while number:
				# Increased Speed Slightly by checking every 5 digits together.
				sum_of_digits_squared += DIGITS_SQUARED[number % 1_0_0_0_0_0]
				number //= 1_0_0_0_0_0

		return sum_of_digits_squared


# There are 2 Chains made,
# One ends with 89 with the chain member 58 being the one which when declared first,
# there will be the least number of iterations for all the members to be checked.

# The other one ends with 1 and has only one element 1.

# So 58 and 1 are chosen to be declared at the starting.

# Changed dictionary to an array to quicken the solution
lowerCAmelCase									=   [None] * 10_00_00_00
lowerCAmelCase									=   True
lowerCAmelCase									=   False






def   __A    (				a_    :	int						):

		if CHAINS[number - 1] is not None:
				return CHAINS[number - 1]  # type: ignore

		lowerCAmelCase  :     Dict       						= chain(next_number(a_						)						)
		lowerCAmelCase  :     Union[str, Any]       						= number_chain

		while number < 1_0_0_0_0_0_0_0:
				lowerCAmelCase  :     Any       						= number_chain
				number *= 1_0

		return number_chain






def   __A    (				a_    :	int = 1_0_0_0_0_0_0_0						):
		for i in range(1   ,a_						):
				if CHAINS[i] is None:
						chain(i + 1						)

		return CHAINS[:number].count(a_						)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					print(F'''{solution() = }''')



